<html>
   <head>
      <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
   
      <link rel="stylesheet" href="./../../helpwin.css">
      <title>MATLAB File Help: prtClassBinaryToMaryOneVsAll/prtClassBinaryToMaryOneVsAll</title>
   </head>
   <body>
      <!--Single-page help-->
      <table border="0" cellspacing="0" width="100%">
         <tr class="subheader">
            <td class="headertitle">MATLAB File Help: prtClassBinaryToMaryOneVsAll/prtClassBinaryToMaryOneVsAll</td>
            
            
         </tr>
      </table>
      <div class="title">prtClassBinaryToMaryOneVsAll/prtClassBinaryToMaryOneVsAll</div>
      <div class="helptext"><pre><!--helptext -->  <span class="helptopic">prtClassBinaryToMaryOneVsAll</span>  M-Ary Emulation Classifier
 
     CLASSIFIER = <span class="helptopic">prtClassBinaryToMaryOneVsAll</span> returns a M-ary "one
     versus all" classifier. A one versus all classifier utilizes a
     binary classifier to make M-ary decisions. For all M classes, it
     selects each class, and makes a binary comparison to all the
     others.
 
     CLASSIFIER = <span class="helptopic">prtClassBinaryToMaryOneVsAll</span>(PROPERTY1, VALUE1, ...)
     constructs a <span class="helptopic">prtClassBinaryToMaryOneVsAll</span> object CLASSIFIER with
     properties as specified by PROPERTY/VALUE pairs.
 
     A <span class="helptopic">prtClassBinaryToMaryOneVsAll</span> object inherits all properties from the
     abstract class prtClass. In addition is has the following
     properties:
 
     baseClassifier - The classifier to be used to make the binary
                   decisions. Must be a prtClass object, and defaults 
                   to a prtClassLogisticDiscriminant classifier.
  
     A <span class="helptopic">prtClassBinaryToMaryOneVsAll</span> object inherits the TRAIN, RUN,
     CROSSVALIDATE and KFOLDS methods from prtAction. It also inherits
     the PLOT method from prtClass.
 
     Example:
 
      TestDataSet = prtDataGenMary;      % Create some test and 
      TrainingDataSet = prtDataGenMary;  % training data
      classifier = <span class="helptopic">prtClassBinaryToMaryOneVsAll</span>;   % Create a classifier
      classifier.baseClassifier = prtClassGlrt;    % Set the binary 
                                                   % Classifier
      % Set the internal Decider
      classifier.internalDecider = prtDecisionMap;
 
      classifier = classifier.train(TrainingDataSet);    % Train
      classes    = run(classifier, TestDataSet);         % Test
 
      % Evaluate, plot results
      percentCorr = prtScorePercentCorrect(classes.getX,TestDataSet.getTargets)
      classifier.plot;</pre></div><!--after help --><!--seeAlso--><div class="footerlinktitle">See also</div><div class="footerlink"> <a href="./../prtClass.html">prtClass</a>, <a href="./../prtClassLogisticDiscriminant.html">prtClassLogisticDiscriminant</a>, <a href="./../prtClassBagging.html">prtClassBagging</a>,
     <a href="./../prtClassMap.html">prtClassMap</a>, <a href="./../prtClassCap.html">prtClassCap</a>, <a href="./../prtClassBinaryToMaryOneVsAll.html">prtClassBinaryToMaryOneVsAll</a>, <a href="./../prtClassDlrt.html">prtClassDlrt</a>,
     <a href="./../prtClassPlsda.html">prtClassPlsda</a>, <a href="./../prtClassFld.html">prtClassFld</a>, <a href="./../prtClassRvm.html">prtClassRvm</a>, <a href="./../prtClassGlrt.html">prtClassGlrt</a>,  <a href="./../prtClass.html">prtClass</a>
</div>
   </body>
</html>